CN102298632B - Character string similarity computing method and device and material classification method and device - Google Patents

Abstract

The invention discloses a character string similarity computing method and device and a material classification method and device. The similarity computing method comprises the following steps of: computing initial similarity between a character string X and a character string di; acquiring the longest common prefix and the longest common postfix between the character string X and the character string di; determining weight of the longest common prefix and weight of the longest common postfix; and computing similarity between the character string X and the character string di. Through the technical scheme, the invention provides a material classification-oriented Chinese character string similarity computing method (namely a dynamic weight method) aiming at the characteristics of names of Chinese materials. By adopting the method, the weight of the prefix and the weight of the postfix of the character string of the name of each material can be dynamically estimated, so that the names of the materials of same type have high similarity, and the accuracy for automatic classification of the materials is improved.

Description

Similarity of character string computing method and device and materials and equipment classification method and device

Technical field

The present invention relates to similarity and calculate and materials and equipment classification field, particularly, relate to a kind of based on forward and backward similarity of character string computing method and device and materials and equipment classification method and the device of sewing changeable weight of character string.

Background technology

The Technologies of Automated Text Classification of comparative maturity has neural network (Neural Net at present, NNet), support vector machine (Support Vector Machine, SVM), simple Bayes (Naive Bayes, NB), k neighbour (k nearest neighbor, k-NN) and linear least square matching (Linear Least Squares Fit, LLSF) etc.These methods are applied to materials and equipment classification, all need to solve the problem that between goods and materials, similarity is calculated.Different from the applied environment of traditional text automatic classification, in enterprise, the Name and Description of goods and materials is often more brief, Text similarity computing method based on word frequency can not meet the needs of materials and equipment classification, so need to calculate the similarity between goods and materials by means of other method, as the similarity of character string.

About the calculating of similarity of character string, in english-speaking environment, set up ripe theory and model, and be widely used.Wherein, from the scholar of statistics, database, artificial intelligence field, all from the research field of self, different similarity calculating methods has been proposed.These methods are in to the match test of all kinds of titles, and Jaro-Winkler and Monge-Elkan behave oneself best, and are more suitable for the coupling of name, place name, organization names.Scholar afterwards finds that approximate string match and language have very large correlativity, so according to the feature of different language, proposed improved algorithm: as Piskorski has proposed improvement algorithm for the Polish family of languages; Arehart etc. are studied for the similarity of roman character string.The research of Chinese character string similarity has also been had to many achievements, and obtained many practical applications.As Li Honglian etc. has proposed a kind of similarity algorithm of applicable speech recognition; Zhou Faguo etc. have proposed the similarity calculating method of sentence for online question answering system; Zhang Chengzhi has proposed the similarity calculating method of the multilayer features such as a kind of integrated literal, semanteme and statistical correlation.

Although the research of text classification and similarity of character string has had a lot of achievements, also there is no the research of classifying for enterprise material under Chinese environment specially.In enterprise, the Name and Description of goods and materials has the feature of himself, so the similarity that needs new technology accurately to measure goods and materials Name and Description improves the accuracy rate of classification.

Summary of the invention

The object of this invention is to provide a kind of based on forward and backward similarity of character string computing method and device and materials and equipment classification method and the device of sewing changeable weight of character string, the method and device can make other goods and materials title of same class have higher similarity, have improved the accuracy rate of goods and materials automatic classifications.

To achieve these goals, the invention provides a kind of similarity of character string computing method, the method comprises: calculating character string X and character string d _ibetween initial similarity Sim, character string d _ifor belonging to a set { C ₁, C ₂... C _nclassification C _ja character string, a plurality of classification C of this set-inclusion, n is the number of classification C, each classification comprises a plurality of character strings; Obtain character string X and character string d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommon; The longest common prefix Prefix described in determining _maxCommonweight PW (Prefix _maxCommon, C _j) and the longest described public suffix Suffix _maxCommonweight SW (Suffix _maxCommon, C _j); And calculating character string X and character string d _ibetween similarity Sim _{dynamicWeight}(X, d _i), computing formula is as follows: Sim _{dynamicWeight}(X, d _i)=Sim+ θ * PW _maxCommon* (1-Sim)+(1-θ) * SW _maxommon* (1-Sim), wherein θ is greater than 0 and be less than 1 merge coefficient.

The present invention separately provides a kind of materials and equipment classification method, and the method comprises: utilize above-mentioned similarity calculating method, calculate the goods and materials title X of goods and materials to be sorted and the goods and materials title d of interior each goods and materials of a plurality of material category _ibetween similarity Sim _{dynamicWeight}(X, d _i); Get K goods and materials title of similarity maximum, form set KNN; According to the classification under the K of similarity maximum goods and materials title, the candidate's class C to goods and materials to be sorted _jmark, evaluate formula is as follows: $p(X,C_j)=\underset{d_i\∈\mathrm{KNN}}{\mathrm{\Σ}}{\mathrm{Sim}}_{\mathrm{DynamicWeight}}(X,d_i)*y(d_i,C_j),$ Y (d wherein _i, C _j) be category attribute function; And according to appraisal result, determine the classification that described goods and materials to be sorted are affiliated.

Correspondingly, the present invention also provides a kind of similarity of character string calculation element, and this device comprises: initial similarity calculation element (10), and for calculating character string X and character string d _ibetween initial similarity Sim, character string d _ifor belonging to a set { C ₁, C ₂... C _nclassification C _ja character string, a plurality of classification C of this set-inclusion, n is the number of classification C, each classification comprises a plurality of character strings; Acquisition device (20) is sewed in public front and back, for obtaining character string X and character string d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommon; Weight determining device (30), for the longest common prefix Prefix described in determining _maxCommonweight PW (Prefix _maxCommon, C _j) and the longest described public suffix Suffix _maxCommonweight SW (Suffix _maxCommon, C _j) and similarity calculation element (40), for calculating character string X and character string d _ibetween similarity Sim _{dynamicWeight}(X, d _i), computing formula is as follows: Sim _{dynamicWeight}(X, d _i)=Sim+ θ * PW _maxCommon* (1-Sim)+(1-θ) * SW _maxCommon* (1-Sim), wherein θ is greater than 0 and be less than 1 merge coefficient.

Correspondingly, the present invention also provides a kind of materials and equipment classification device, and this device comprises: above-mentioned similarity of character string calculation element (100), and for calculating the goods and materials title d of each goods and materials in the goods and materials title X of goods and materials to be sorted and a plurality of material category _ibetween similarity Sim _{dynamicWeight}(X, d _i); Similarity maximum set determining device (200), for getting K goods and materials title of similarity maximum, forms set KNN; Scoring apparatus (300), for according to the classification under the K of similarity maximum goods and materials title, the candidate's class C to goods and materials to be sorted _jmark, evaluate formula is as follows: $p(X,C_j)=\underset{d_i\∈\mathrm{KNN}}{\mathrm{\Σ}}{\mathrm{Sim}}_{\mathrm{DynamicWeight}}(X,d_i)*y(d_i,C_j),$ Y (d wherein _i, C _j) be category attribute function; And classification determining device (400), for according to appraisal result, determine the classification that described goods and materials to be sorted are affiliated.

Pass through technique scheme, feature for Chinese goods and materials title, (the present invention has provided a kind of Chinese character string similarity calculating method towards materials and equipment classification, changeable weight method (DynamicWeight)), it dynamically estimates the forward and backward weight of sewing of goods and materials name character string, make other goods and materials title of same class there is higher similarity, improved the accuracy rate of goods and materials automatic classifications.

Other features and advantages of the present invention partly in detail are described the embodiment subsequently.

Accompanying drawing explanation

Accompanying drawing is to be used to provide a further understanding of the present invention, and forms a part for instructions, is used from explanation the present invention, but is not construed as limiting the invention with embodiment one below.In the accompanying drawings:

Fig. 1 is provided by the invention based on the forward and backward process flow diagram of sewing the similarity of character string computing method of changeable weight of character string;

Fig. 2 is the forward and backward process flow diagram that weight is estimated of sewing;

Fig. 3 is the process flow diagram of materials and equipment classification provided by the invention;

Fig. 4 adopts respectively the accuracy rate that Jaro-Winkler, Monge-Elkan and DynamicWeight of the present invention classify to 9 large classes to compare schematic diagram;

Fig. 5 is for adopting respectively Jaro-Winkler, Monge-Elkan and DynamicWeight of the present invention to 60 large classes, 660 middle classes and 3940 accuracy rate comparison schematic diagram that group is classified;

Fig. 6 adopts respectively the recall rate that Jaro-Winkler, Monge-Elkan and DynamicWeight of the present invention classify to 3940 groups to compare schematic diagram;

Fig. 7 is provided by the invention based on the forward and backward block diagram of sewing the similarity of character string calculation element of changeable weight of character string; And

Fig. 8 is the block diagram of materials and equipment classification device.

Description of reference numerals

Acquisition device is sewed in the 10 public front and back of initial similarity calculation element 20

30 weight determining device 40 similarity calculation elements

100 similarity of character string calculation element 200 similarity maximum set determining devices

300 scoring apparatus 400 classification determining devices

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is elaborated.Should be understood that, embodiment described herein only, for description and interpretation the present invention, is not limited to the present invention.

Fig. 1 is provided by the invention based on the forward and backward process flow diagram of sewing the similarity of character string computing method of changeable weight of character string.As shown in Figure 1, the invention provides a kind of similarity of character string computing method, the method comprises: calculating character string X and character string d _ibetween initial similarity Sim, character string d _ifor belonging to a set { C ₁, C ₂... C _nclassification C _ja character string, a plurality of classification C of this set-inclusion, n is the number of classification C, each classification comprises a plurality of character strings; Obtain character string X and character string d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommon; The longest common prefix Prefix described in determining _maxCommonweight PW (Prefix _maxCommon, C _j) and the longest described public suffix Suffix _maxCommonweight SW (Suffix _maxCommon, C _j); Calculating character string X and character string d _ibetween similarity Sim _{dynamicWeight}(X, d _i), computing formula is as follows: Sim _{dynamicWeight}(X, d _i)=Sim+ θ * PW _maxCommon* (1-Sim)+(1-θ) * SW _maxCommon* (1-Sim), wherein θ is greater than 0 and be less than 1 merge coefficient, and this merge coefficient can be set the forward and backward impact of weight on similarity of sewing, and generally can be set as 0.5, and the forward and backward weight of sewing is identical on the impact of similarity.

The computing formula of described initial similarity can be:

Sim＝1/3(m/|length(X)|+m/|length(d _i)|+(m-t)/m)，

Wherein, m is character string X and character string d _ithe character number matching, length (X) and length (d _i) represent respectively character string X and character string d _icharacter, t represents character string X and character string d _iin the process matching, the number of times that character position changes, half of number that is the coupling character of different order is the number t of transposition, for instance, MARTHA mates with the character of MARHTA, but in the character of these couplings, T and H will replace and MARTHA could be become to MARHTA, T and H are exactly the coupling character of different order so, t=2/2=1.The similarity that this formula calculates is Jaro similarity, and certainly, the present invention is not limited to this, and other formula that can realize similar similarity calculating also can be applicable to this.

Described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula can be:

$\mathrm{PW}({\mathrm{Prefix}}_{\mathrm{MaxCommon}},C_j)=\mathrm{Freq}(\mathrm{Cateogy}=C_j|\mathrm{Prefix}={\mathrm{Prefix}}_{\mathrm{MaxCommon}})$

$=\frac{N(\mathrm{Cateogy}=C_j,\mathrm{Prefix}={\mathrm{Prefix}}_{\mathrm{MaxCommon}})}{N(\mathrm{Prefix}={\mathrm{Prefix}}_{\mathrm{MaxCommon}})}$

$\mathrm{PW}({\mathrm{Suffix}}_{\mathrm{MaxCommon}},C_j)=\mathrm{Freq}(\mathrm{Cateogy}=C_j|\mathrm{Suffix}={\mathrm{Suffix}}_{\mathrm{MaxCommon}})$

$=\frac{N(\mathrm{Cateogy}=C_j,\mathrm{Suffix}={\mathrm{Suffix}}_{\mathrm{MaxCommon}})}{N(\mathrm{Suffix}={\mathrm{Suffix}}_{\mathrm{MaxCommon}})}$

Wherein, N (Cateogy=C _j, Prefix=Prefix _maxCommon) represent described set { C ₁, C ₂... C _nin prefix be Prefix _maxCommonand belong to classification C _jthe number of character string, N (Cateogy=C _j, Suffix=Suffix _maxCommon) represent described set { C ₁, C ₂... C _nin suffix be Suffix _maxCommonand belong to classification C _jthe number of character string, N (Prefix=Prefix _maxCommon) represent described set { C ₁, C ₂... C _nin prefix be Prefix _maxCommonthe number of character string, N (Suffix=Suffix _maxCommon) represent described set { C ₁, C ₂... C _nin suffix be Suffix _maxCommonthe number of character string.

Preferably, can be to above-mentioned weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) carrying out smoothing processing, concrete formula is as follows:

PW(Prefix _MaxCommon，C _j)＝α*Freq(Cateogy＝C _j|Prefix＝Prefix _MaxCommon)+(1-α)/n

PW(Suffix _MaxCommon，C _j)＝β*Freq(Cateogy＝C _j|Suffix＝Suffix _MaxCommon)+(1-β)/n

Wherein, α and β are greater than 0 and be less than 1 merge coefficient, this merge coefficient can be set respectively the forward and backward probability that occurs in the classification impact on weight of sewing, and generally can be set as 0.9, sets that forward and backward to sew the probability occurring in classification very large on the impact of weight.

Fig. 2 is the forward and backward process flow diagram that weight is estimated of sewing.As shown in Figure 2, can be to described set { C ₁, C ₂... C _nin each character string (this character string can comprise Chinese character and western language character) of each classification, get successively from front to back substring, western language character is made Chinese character and is processed, the reference position of substring is the first character of described character string, end position starts to increase progressively successively from first position, until the afterbody of described character string, this substring can be designated as Prefix _i, be prefix.To described set { C ₁, C ₂... C _nin each character string (this character string can comprise Chinese character and western language character) of each classification, get successively from back to front substring, western language character is made Chinese character and is processed, the end position of substring is the afterbody of described character string, reference position starts to increase progressively successively from the afterbody of described character string, until the head of described character string, this substring can be designated as Suffix _i, be suffix.By this, can utilize above-mentioned weight calculation formula (can comprise smoothing processing), pair set { C ₁, C ₂... C _nin each classification each character string each possible forward and backward sewing carry out weight estimation (, calculate each possible forward and backward probability occurring of sewing in each classification, can carry out smoothing processing afterwards), obtain each possible forward and backward weight of sewing, thereby build the forward and backward weight tables of data of sewing.Thereby, at calculating character string X and character string d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommonweight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) time, can be directly in this forward and backward sewing, in weight tables of data, obtain weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j).

Fig. 3 is the process flow diagram of materials and equipment classification provided by the invention.As shown in Figure 3, the present invention separately provides a kind of materials and equipment classification method that adopts above-mentioned similarity of character string computing method, the method comprises: utilize above-mentioned similarity calculating method, calculate the goods and materials title X of goods and materials to be sorted and the goods and materials title d of interior each goods and materials of a plurality of material category _ibetween similarity Sim _{dynamicWeight}(X, d _i); Get K goods and materials title of similarity maximum, form set KNN (K-NearestNeighbour); According to the classification under the K of similarity maximum goods and materials title, the candidate's class C to goods and materials to be sorted _jmark, evaluate formula is as follows: $p(X,C_j)=\underset{d_i\∈\mathrm{KNN}}{\mathrm{\Σ}}{\mathrm{Sim}}_{\mathrm{DynamicWeight}}(X,d_i)*y(d_i,C_j),$ Y (d wherein _i, C _j) be category attribute function; And according to appraisal result, determine the classification that described goods and materials to be sorted are affiliated.In this materials and equipment classification method, in above-mentioned similarity calculating method, mentioned " character string " is " the goods and materials title " of goods and materials.

Wherein, described y (d _i, C _j) be category attribute function, if d _ibelong to classification C _j, the functional value of this function is 1, otherwise is 0.Certainly, the present invention is not limited to this, also can adopt other values, as long as can realize differentiation by level scoring, such as can be in d _ibelong to classification C _j, the functional value of this function is 0.9, otherwise is 0.

By above-mentioned evaluate formula, can show that goods and materials to be sorted are arranged in affiliated each classification of classification (that is, the candidate's class C of K goods and materials title of similarity maximum _j) in possibility, can, according to this possibility, determine the classification of goods and materials to be sorted afterwards.For some goods and materials to be sorted, it can be divided into a plurality of candidate's classes (that is, having plurality of classes), thereby a threshold value need be set, described appraisal result is sorted, obtain minute the highest previous or a plurality of classifications (such other quantity is determined by described threshold value).For general situation, the situation that threshold value is 1, gets p (X, C _j) maximum C _jas the classification under described goods and materials to be sorted.

The beneficial effect of materials and equipment classification method of the present invention is described by specific embodiment below.According to the three phases of technology contents, implementation step is also divided into three phases.

one, weight estimation stages:

Step 1: in weight estimation stages, first will define the classification of goods and materials.In this example, provide seven classifications conventional in materials and equipment classification: common iron, non-ferrous metal and rapidoprint, architectural hardware, nonmetallic materials, textiles and other light industrial goods, timber and goods and daily-use electrical appliance.

Step 2: then for some goods and materials titles are added in each classification, as the goods and materials title of adding in " architectural hardware " classification has: aluminum alloy casement window, aluminium alloy screen, aluminium alloy window, safety lockset kit, flexible wirerope Circuit lock, six hole locksets, portable lock box, wirerope lockset, butterfly valve lockset, safe off-stream unit lock tube, switch lockset, lockset link plate etc. altogether.The classification of these goods and materials and correspondence thereof is as training dataset (that is, above-mentioned set { C ₁, C ₂... C _n).

Step 3: each the goods and materials title to training dataset, list its prefix and suffix.If the prefix sets of goods and materials " aluminium alloy screen " is { aluminium, aluminium closes, aluminium alloy, aluminium alloy yarn, aluminium alloy screen }, its suffix set is { window, screen window, golden screen window, alloy screen window, aluminium alloy screen }.

Step 4: add up the probability that each prefix and suffix occur in each classification.

Step 5: the probability counting is carried out to smoothing processing, and take suffix " window " and " plate " is example, probability that it occurs in each classification and level and smooth after the weight that obtains as shown in table 1.

Probability and weight that table 1 suffix " window " and " plate " occur in each classification

two, similarity calculation stages

This stage is usingd " aluminum alloy slide window " as the goods and materials title of goods and materials to be sorted, and " aluminum alloy casement window " is as the goods and materials title of certain goods and materials in training set.

Step 1: the Jaro similarity of calculating " aluminum alloy slide window " and " aluminum alloy casement window ", the length of these two character strings is all 6, the character number of coupling is 4, and the number of times that position changes is 0, and the similarity obtaining according to the computing formula of Jaro is 0.778.

Step 2: comparison " aluminum alloy slide window " and " aluminum alloy casement window ", the longest common prefix is " aluminium alloy ", the longest public suffix is " window ".

Step 3: because " aluminum alloy casement window " belongs to architectural hardware classification, training to forward and backwardly find corresponding weight estimated value: PW (aluminium alloy, architectural hardware)=0.587, SW (window, architectural hardware)=0.529 in sewing weight sets.

Step 4: the Sim that calculates " aluminum alloy slide window " and " aluminum alloy casement window " _{dynamicWeight}similarity, wherein set merge coefficient θ=0.5, the similarity obtaining is 0.902.

three, the automatic classification stage

Step 1: the goods and materials title " aluminum alloy slide window " of inputting goods and materials to be sorted.

Step 2: the similarity of calculating each goods and materials title in " aluminum alloy slide window " and training set according to the method in the stage 2.

Step 3: find front k the goods and materials title with " aluminum alloy slide window " similarity maximum.Herein, k is set as 5, and the most similar 5 goods and materials titles, similarity and the classification found are as table 2.

	Goods and materials title	Similarity	Affiliated classification
				1	Aluminum alloy slide window material	0.973	Non-ferrous metal and rapidoprint
2	Aluminium alloy screen	0.921	Architectural hardware
				3	Aluminum alloy casement window	0.902	Architectural hardware
4	Aluminium alloy window	0.902	Architectural hardware
				5	Aluminium alloy wire	0.823	Non-ferrous metal and rapidoprint

Table 2 training set neutralization 5 the most similar goods and materials titles of " aluminum alloy slide window "

Step 4: according to the situation of classifying under front 5 goods and materials the most similar, calculate p (aluminum alloy slide window, architectural hardware)=2.725, p (aluminum alloy slide window, non-ferrous metal and rapidoprint)=1.875.

Step 5: according to the result calculating, p (aluminum alloy slide window, architectural hardware) maximum, when Output rusults number is 1, " aluminum alloy slide window " assigned to " architectural hardware " this classification automatically.

Classifying quality of the present invention as shown in Figure 4, Figure 5 and Figure 6, for the similarity of character string based on changeable weight that proposes in the illustration the present invention validity for materials and equipment classification, also with contrast with Jaro-Winkler and two kinds of similarity calculating methods of Monge-Elkan simultaneously.The evaluation of materials and equipment classification is from the viewpoint of accuracy and spend two comprehensively, and leading indicator is accuracy rate and recall rate.The computing formula of accuracy rate is: the number of classification in the correct number/Output rusults of classifying in accuracy rate (Precision)=Output rusults; The computing formula of recall rate is: the number of the classification of the correct number of classifying in recall rate (Recall)=Output rusults/should be divided into.Accuracy rate mainly pays close attention to how much in Output rusults, have be correct; Recall rate is mainly paid close attention to correct classification results to be had and how much has been covered to.

Fig. 4 adopts respectively the accuracy rate that Jaro-Winkler, Monge-Elkan and DynamicWeight of the present invention classify to 9 large classes to compare schematic diagram.As can be seen from Figure 4, three kinds of similarity of character string methods are effective centering historical relic money title automatic classification all, and the classification accuracy that the Dynamic-Weight method based on changeable weight obtains is all better than Jaro-Winkler and Monge-Elkan method in each classification.

Fig. 5 is for adopting respectively Jaro-Winkler, Monge-Elkan and DynamicWeight of the present invention to 60 large classes, 660 middle classes and 3940 accuracy rate comparison schematic diagram that group is classified.From Fig. 5 can, along with the granularity of classification is more and more thinner, the classification accuracy of the whole bag of tricks all obviously declines.But the Dynamic-Weight method based on changeable weight is all the highest in the accuracy rate of three category level

Fig. 6 adopts respectively the recall rate that Jaro-Winkler, Monge-Elkan and DynamicWeight of the present invention classify to 3940 groups to compare schematic diagram.As can be seen from Figure 6, along with Output rusults number (, described threshold value) increase, the recall rate of automatic classification (Recall) is significantly improved, and in three kinds of methods, Dynamic-Weight method based on changeable weight above three curves, has shown that this method is stable all the time.

The invention has the advantages that the actual features according to Chinese materials and equipment classification, designed the similarity of character string computing method towards Chinese goods and materials title.This method estimates the forward and backward weight of sewing in each materials and equipment classification of goods and materials title by training, when comparing goods and materials title, has increased the similarity of one species goods and materials, thereby has improved the accuracy rate of goods and materials automatic classifications.

Fig. 7 is provided by the invention based on the forward and backward block diagram of sewing the similarity of character string calculation element of changeable weight of character string.Correspondingly, as shown in Figure 7, the present invention also provides a kind of similarity of character string calculation element, and this device comprises: initial similarity calculation element 10, and for calculating character string X and character string d _ibetween initial similarity Sim, character string d _ifor belonging to a set { C ₁, C ₂... C _nclassification C _ja character string, a plurality of classification C of this set-inclusion, n is the number of classification C, each classification comprises a plurality of character strings; Acquisition device 20 is sewed in public front and back, for obtaining character string X and character string d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommon; Weight determining device 30, for the longest common prefix Prefix described in determining _maxCommonweight PW (Prefix _maxCommon, C _j) and the longest described public suffix Suffix _maxCommonweight SW (Suffix _maxCommon, C _j) and similarity calculation element 40, for calculating character string X and character string d _ibetween similarity Sim _{dynamicWeight}(X, d _i), computing formula is as follows: Sim _{dynamicWeight}(X, d _i)=Sim+ θ * PW _maxCommon* (1-Sim)+(1-θ) * SW _maxCommon* (1-Sim), wherein θ is greater than 0 and be less than 1 merge coefficient.

Wherein, the computing formula of described initial similarity can be:

Sim=1/3 (m/|length (X) |+m/|length (d _i) |+(m-t)/m, wherein m is character string X and character string d _ithe character number matching, length (X) and length (d _i) represent respectively character string X and character string d _icharacter, t represents character string X and character string d _iin the process matching, the number of times that character position changes.

Wherein, described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula can be:

PW(Prefix _MaxCommon，C _j)＝Freq(Cateogy＝C _j|Prefix＝Prefix _MaxCommon)

PW(Suffix _MaxCommon，C _j)＝Freq(Cateogy＝C _j|Suffix＝Suffix _MaxCommon)

Wherein, Freq (Cateogy=C _j| Prefix=Prefix _maxCommon) expression prefix is Prefix _maxCommoncharacter string in classification C _jthe probability of interior appearance, Freq (Cateogy=C _j| Suffix=Suffix _maxCommon) expression suffix is Suffix _maxCommoncharacter string in classification C _jthe probability of interior appearance.

Wherein, described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula can be:

PW(Prefix _MaxCommon，C _j)＝α*Freq(Cateogy＝C _j|Prefix＝Prefix _MaxCommon)+(1-α)/n

PW(Suffix _MaxCommon，C _j)＝β*Freq(Cateogy＝C _j|Suffix＝Suffix _MaxCommon)+(1-β)/n

Wherein, α and β are greater than 0 and be less than 1 merge coefficient.

About detail that should be based on the forward and backward similarity of character string calculation element of sewing changeable weight of character string and beneficial effect are with above-mentioned identical for the description based on the forward and backward similarity of character string computing method of sewing changeable weight of character string, in this, repeat no more.

Fig. 8 is the block diagram of materials and equipment classification device.Correspondingly, as described in Figure 8, the present invention also provides a kind of materials and equipment classification device, and this device comprises: above-mentioned similarity of character string calculation element 100, and for calculating the goods and materials title d of each goods and materials in the goods and materials title X of goods and materials to be sorted and a plurality of material category _ibetween similarity Sim _{dynamicWeight}(X, d _i); Similarity maximum set determining device 200, for getting K goods and materials title of similarity maximum, forms set KNN; Scoring apparatus 300, for according to the classification under the K of similarity maximum goods and materials title, the candidate's class C to goods and materials to be sorted _jmark, evaluate formula is as follows: $p(X,C_j)=\underset{d_i\∈\mathrm{KNN}}{\mathrm{\Σ}}{\mathrm{Sim}}_{\mathrm{DynamicWeight}}(X,d_i)*y(d_i,C_j),$ Y (d wherein _i, C _j) be category attribute function; And classification determining device 400, for according to appraisal result, determine the classification that described goods and materials to be sorted are affiliated.

Wherein, the desirable p of described classification determining device 400 (X, C _j) maximum C _jas the classification under described goods and materials to be sorted.

About detail and the beneficial effect of this materials and equipment classification device are identical with the above-mentioned description for materials and equipment classification method, in this, repeat no more.

Below describe by reference to the accompanying drawings the preferred embodiment of the present invention in detail; but; the present invention is not limited to the detail in above-mentioned embodiment; within the scope of technical conceive of the present invention; can carry out multiple simple variant to technical scheme of the present invention, these simple variant all belong to protection scope of the present invention.

It should be noted that in addition each the concrete technical characterictic described in above-mentioned embodiment, in reconcilable situation, can combine by any suitable mode.For fear of unnecessary repetition, the present invention is to the explanation no longer separately of various possible array modes.

In addition, between various embodiment of the present invention, also can carry out combination in any, as long as it is without prejudice to thought of the present invention, it should be considered as content disclosed in this invention equally.

Claims (10)

1. a materials and equipment classification method, the method comprises:

Calculate the goods and materials title X of goods and materials to be sorted and the goods and materials title d of interior each goods and materials of a plurality of material category _ibetween similarity Sim _{dynamicWeight}(X, d _i);

Get K goods and materials title of similarity maximum, form set KNN;

According to the classification under the K of similarity maximum goods and materials title, the candidate's class C to goods and materials to be sorted _jmark, evaluate formula is as follows: $p(X,C_j)=\underset{d_i\∈\mathrm{KNN}}{\mathrm{\Σ}}{\mathrm{Sim}}_{\mathrm{DynamicWeight}}(X,d_i)*y(d_i,C_j),$ Y (d wherein _i, C _j) be category attribute function; And

According to appraisal result, determine the classification that described goods and materials to be sorted are affiliated,

Wherein calculating described similarity comprises:

Calculate the goods and materials title d of each goods and materials in goods and materials title X and a plurality of material category _ibetween initial similarity Sim, goods and materials title d _ifor belonging to a set { C ₁, C ₂... C _nclassification C _ja goods and materials title, a plurality of classification C of this set-inclusion, n is the number of classification C, each classification comprises a plurality of goods and materials titles;

Obtain goods and materials title X and goods and materials title d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommon;

The longest common prefix Prefix described in determining _maxCommonweight PW (Prefix _maxCommon, C _j) and the longest described public suffix Suffix _maxCommonweight SW (Suffix _maxCommon, C _j); And

Calculate goods and materials title X and goods and materials title d _ibetween similarity Sim _{dynamicWeight}(X, d _i), computing formula is as follows: Sim _dnamicWeight(X, d _i)=Sim+ θ * PW _maxCommon* (1-Sim)+(1-θ) * SW _maxCommon* (1-Sim), wherein θ is greater than 0 and be less than 1 merge coefficient.

2. method according to claim 1, is characterized in that, the computing formula of described initial similarity Sim is as follows:

Sim=1/3 (m/|length (X) |+m/|length (d _i) |+(m-t)/m), wherein m is goods and materials title X and goods and materials title d _ithe character number matching, length (X) and length (d _i) represent respectively goods and materials title X and goods and materials title d _icharacter, t represents goods and materials title X and goods and materials title d _iin the process matching, the number of times that character position changes.

3. method according to claim 1, is characterized in that, described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula as follows:

PW(Prefix _MaxCommon,C _j)=Freq(Cateogy=C _j|Prefix=Prefix _MaxCommon)

PW(Suffix _MaxCommon,C _j)=Freq(Cateogy=C _j|Suffix=Suffix _MaxCommon)

Wherein, Freq (Cateogy=C _j| Prefix=Prefix _maxCommon) expression prefix is Prefix _maxCommongoods and materials title in classification C _jthe probability of interior appearance, Freq (Cateogy=C _j| Suffix=Suffix _maxCommon) expression suffix is Suffix _maxCommongoods and materials title in classification C _jthe probability of interior appearance.

4. method according to claim 1, is characterized in that, described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula as follows:

PW(Prefix _MaxCommon,C _j)=α*Freq(Cateogy=C _j|Prefix=Prefix _MaxCommon)+(1-α)/n

PW(Suffix _MaxCommon,C _j)=β*Freq(Cateogy=C _j|Suffix=Suffix _MaxCommon)+(1-β)/n

Wherein, α and β are greater than 0 and be less than 1 merge coefficient.

5. method according to claim 1, is characterized in that, the classification under described definite described goods and materials to be sorted comprises: get p (X, C _j) maximum C _jas the classification under described goods and materials to be sorted.

6. a materials and equipment classification device, this device comprises:

Similarity of character string calculation element (100), for calculating the goods and materials title d of each goods and materials in the goods and materials title X of goods and materials to be sorted and a plurality of material category _ibetween similarity Sim _{dynamicWeight}(X, d _i);

Similarity maximum set determining device (200), for getting K goods and materials title of similarity maximum, forms set KNN;

Scoring apparatus (300), for according to the classification under the K of similarity maximum goods and materials title, the candidate's class C to goods and materials to be sorted _jmark, evaluate formula is as follows: $p(X,C_j)=\underset{d_i\∈\mathrm{KNN}}{\mathrm{\Σ}}{\mathrm{Sim}}_{\mathrm{DynamicWeight}}(X,d_i)*y(d_i,C_j),$ Y (d wherein _i, C _j) be category attribute function; And

Classification determining device (400), for according to appraisal result, determines the classification that described goods and materials to be sorted are affiliated,

Wherein said similarity of character string calculation element (100) comprising:

Initial similarity calculation element (10), for calculating goods and materials title X and goods and materials title d _ibetween initial similarity Sim, goods and materials title d _ifor belonging to a set { C ₁, C ₂... C _nclassification C _ja goods and materials title, a plurality of classification C of this set-inclusion, n is the number of classification C, each classification comprises a plurality of goods and materials titles;

Acquisition device (20) is sewed in public front and back, for obtaining goods and materials title X and goods and materials title d _ibetween the longest common prefix Prefix _maxCommonwith the longest public suffix Suffix _maxCommon;

Weight determining device (30), for the longest common prefix Prefix described in determining _maxCommonweight PW (Prefix _maxCommon, C _j) and the longest described public suffix Suffix _maxCommonweight SW (Suffix _maxCommon, C _j); And

Similarity calculation element (40), for calculating goods and materials title X and goods and materials title d _ibetween similarity Sim _{dynamicWeight}(X, d _i), computing formula is as follows: Sim _{dynamicWeight}(X, d _i)=Sim+ θ * PW _maxCommon* (1-Sim)+(1-θ) * SW _maxCommon* (1-Sim), wherein θ is greater than 0 and be less than 1 merge coefficient.

7. materials and equipment classification device according to claim 6, is characterized in that, the computing formula of described initial similarity Sim is as follows:

Sim=1/3 (m/|length (X) |+m/|length (d _i) |+(m-t)/m), wherein m is goods and materials title X and goods and materials title d _ithe character number matching, length (X) and length (d _i) represent respectively goods and materials title X and goods and materials title d _icharacter, t represents goods and materials title X and goods and materials title d _iin the process matching, the number of times that character position changes.

8. materials and equipment classification device according to claim 6, is characterized in that, described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula as follows:

PW(Prefix _MaxCommon,C _j)=Freq(Cateogy=C _j|Prefix=Prefix _MaxCommon)

PW(Suffix _MaxCommon,C _j)=Freq(Cateogy=C _j|Suffix=Suffix _MaxCommon)

Wherein, Freq (Cateogy=C _j| Prefix=Prefix _maxCommon) expression prefix is Prefix _maxCommongoods and materials title in classification C _jthe probability of interior appearance, Freq (Cateogy=C _j| Suffix=Suffix _maxCommon) expression suffix is Suffix _maxCommongoods and materials title in classification C _jthe probability of interior appearance.

9. materials and equipment classification device according to claim 6, is characterized in that, described weight PW (Prefix _maxCommon, C _j) and SW (Suffix _maxCommon, C _j) computing formula as follows:

PW(Prefix _MaxCommon,C _j)=α*Freq(Cateogy=C _j|Prefix=Prefix _MaxCommon)+(1-α)/n

PW(Suffix _MaxCommon,C _j)=β*Freq(Cateogy=C _j|Suffix=Suffix _MaxCommon)+(1-β)/n

Wherein, α and β are greater than 0 and be less than 1 merge coefficient.

10. materials and equipment classification device according to claim 6, is characterized in that, described classification determining device (400) is got p (X, C _j) maximum C _jas the classification under described goods and materials to be sorted.